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Focused-Region segmentation for light field images based on PCNN
Light field can generate a set of refocused images by integrating rays into any image plane. It is useful to segment focus region from the refocused image because the focus region contains depth clues. However, to the best of our knowledge, focus-region segmentation for light field images has not been reported. We firstly transform the refocused image into the space of neuron ignition sequence with pulse coupled neural networks (PCNN). Then the model of neuron ignition sequence and the classification criteria are established to divide pixels into different regions. Finally, the clearest region is selected as the segmentation result by the sliding window technology. In this paper, we analyze the features of focus/defocus information in the space of neuron ignition sequence, and propose some simple criteria for pixel classification. The proposed algorithm and other two typical segmentation algorithms for low depth-of-field images are tested on the data set of light field images. The experimental results show that the three algorithms have similar error rates of focused-region segmentation, but the computational time of ours is one order of magnitude shorter than that of the other two.
Focused-Region segmentation for light field images based on PCNN
Light field can generate a set of refocused images by integrating rays into any image plane. It is useful to segment focus region from the refocused image because the focus region contains depth clues. However, to the best of our knowledge, focus-region segmentation for light field images has not been reported. We firstly transform the refocused image into the space of neuron ignition sequence with pulse coupled neural networks (PCNN). Then the model of neuron ignition sequence and the classification criteria are established to divide pixels into different regions. Finally, the clearest region is selected as the segmentation result by the sliding window technology. In this paper, we analyze the features of focus/defocus information in the space of neuron ignition sequence, and propose some simple criteria for pixel classification. The proposed algorithm and other two typical segmentation algorithms for low depth-of-field images are tested on the data set of light field images. The experimental results show that the three algorithms have similar error rates of focused-region segmentation, but the computational time of ours is one order of magnitude shorter than that of the other two.
Focused-Region segmentation for light field images based on PCNN
Gao, Qiang (Autor:in) / Han, Lei (Autor:in) / Shen, Jie (Autor:in) / Wang, Huibin (Autor:in) / Xu, Lizhong (Autor:in)
01.09.2017
519973 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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